Error diffusion for printing

ABSTRACT

There are disclosed techniques for error diffusion for printing.For example, there is disclosed a device comprising a pixel selector, to select a pixel from an image to be printed, wherein the pixel is associated to a group of device state probabilities, wherein each device state probability describes a probability of choosing a particular device state, wherein each device state describes the quantity of each colorant to be used in correspondence of the pixel.The device may comprise a device state determiner, to choose the device state for the selected pixel on the basis of the device state probabilities associated to the selected pixel. The device may comprise a feedback chain, to diffuse device state probability errors to pixels to be subsequently selected. The feedback chain may include a selected pixel error determiner, to determine device state probability errors for each device state probability of the selected pixel. The feedback chain may include a neighbouring pixel selector, to choose neighbouring pixels among the pixels to be subsequently selected. The feedback chain may include a neighbouring pixel error modifier, to modify, for each neighbouring pixel, the determined device state probability errors according to criteria conditioned by metrics and/or classification data associated to the selected pixel and/or a group of previously selected pixels and the relative position between the neighbouring pixel and the selected pixel. The feedback chain may include an error diffuser, to diffuse the modified device state probability errors to the neighbouring pixels.

BACKGROUND

Imaging systems may prepare images to be represented (e.g., printed anddisplayed). In imaging systems based on a device state probabilitypipeline, such as the Halftone Area Neugebauer Separation (HANS)pipeline, colors are treated as statistical distributions of devicestates. A per-pixel decision determines the final color state for therepresentation of each pixel.

DESCRIPTION OF THE FIGURES

FIG. 1 shows a device according to an example;

FIGS. 2a-2d show different patterns of error diffusion according toexamples;

FIGS. 3-5 show devices according to examples.

FIG. 6 shows a method according to an example.

FIG. 7 shows a non-transitory storage unit according to an example.

DETAILED DESCRIPTION

Hereinafter, examples are mainly directed to printing systems andmethods, e.g., using ink-jet printers, laser printers, xerographicprinters, three-dimensional (3D) printers or other printers.

Print permits to represent images to human eye or obtain objects. Printmay be bi-dimensional (2D) print or three-dimensional (3D) print. 2Dprint may be the result of a number of colorants of different colorsdisposed on top of a substrate (e.g., paper). Colorant (e.g., ink)amounts are chosen for each printable color. With 3D print, athree-dimensional object may be obtained by printing on a bed of buildmaterial. A printing system may include a printer, which may be a 2Dprinter or a 3D printer. In certain cases, the printer may be an inkjetprinter, for example a scanning inkjet printer or a page wide arrayprinter. The printing system may comprise a plurality of print elements.A print element may be a print head or die. A page-wide printer may use,for example, an array of print heads, each print head being a printelement. A print head may include a plurality of nozzles, for example aprint head may consist of silicon pieces known as dies in which theprinting nozzles are formed. Each nozzle may be arranged to depositdrops of a printing fluid, such as ink, gloss or varnish. There may be aset amount of ink that is released in each drop, e.g., a large drop mayhave a different volume of ink to a small drop. Certain printers maydeposit a plurality of ink drops when an instruction is received toactuate the nozzles, e.g., the printer may receive a command based onimage data to deposit drops of ink for a given pixel.

A color may be defined with reference to a particular representationmodel, such as Red-Green-Blue, RGB, color space orCyan-Magenta-Yellow-Black, CMYK, color space. Other color models includethe International Commission on Illumination (CIE) 1931 XYZ color space,wherein three variables (X, ‘Y’ and ‘Z’ or tristimulus values) are usedto model a color, and the CIE 1976 (L*, a*, b*-CIELAB) color space,wherein three variables represent lightness (‘L*’) and opposing colordimensions (‘a*’ and ‘b*’).

A colorant may be a print material, e.g., ink, toner, fluid, varnish,etc. The colorant may be defined with reference to a color space (which,in this case, may also be referred to as colorant space), whichcomprises the colors that may be obtained by a particular printer (orcomputed in a particular printing system).

For each pixel, the colorant to be actually applied on the substrate maybe defined in a different space (device space). For example, theNeugebauer primary (NP) space includes all the possible combinationsthat may be chosen for one pixel. If a printer produces one single dropof one single ink for each pixel, a NP can be one of 2^(k) combinationsof k inks. If the colorants are Cyan, Magenta, Yellow (CMY), eight NPsare defined: C, M, Y, C+M (referred to as CM), C+Y (CY), M+Y (MY), C+M+Y(CMY), and K (Blank indicating an absence of ink, which may be white incase of white paper). It may also be possible to use multi-levelprinters, whose print heads are able to deposit N drop levels: a NP maybe for example, one of (N+1)^(k) combinations. If, for example, twodrops of ink can be applied for each of three inks, 3³ NPs are defined.

Halftoning techniques may permit representing images, originallyexpressed as continuous tones/intensities (e.g., grey scales), by usinga limited number of inks (e.g., only black and white). Human eyes tendto filter images. For example, humans perceive patches of black andwhite marks as some kinds of average grey when viewed sufficiently faraway.

An example of halftoning technique is the HANS pipeline, in thistechnique colors are treated as statistical distributions of devicestates. An original image data can comprise color data as represented ina color space (e.g., pixel representations in an Red-Green-Blue, RGBcolor space, represented as intensities). Then, the color data can bemapped from the first (RGB) color space to a device state probabilityspace, such as the Neugebauer Primary area coverage (NPac) space, sothat an image comprises pixels whose color values are defined in termsof probability distributions for each particular NP and for eachparticular pixel.

Each pixel of an image may be described with a group of device stateprobabilities, e.g., a NPac vector. Each component of the NPac vectordefines the probability of choosing a particular NP. For example, aNPac₁ vector could define the following probabilities for each NP:

-   -   1/9 for W (blank or White, where the substrate is white);    -   0 for C;    -   2/9 for M;    -   0 for Y;    -   3/9 for CM;    -   1/9 for CY;    -   1/9 for MY;    -   1/9 for CMY.

In this case, the highest probability is associated to the device stateCM (one drop of Cyan and one Drop of Magenta), which accordingly has thehighest likelihood of being chosen as final device state.

Once an image is defined in terms of device state probabilities (e.g.,NPacs), a choice is to be made to determine the actual device state foreach pixel (e.g., the choice of one particular NP from the NPac vector).Such a determination is in general not an easy task, as it may causeunwanted artefacts. If, for example, the determination of the actualdevice state is performed by always choosing the NP with highest valuein the NPac vector, the image will suffer, for examples, of quantizationerrors and will appear unnatural to a human observer. In some cases, theimage may appear posterized: it can suffer of a low variety of colors,presenting a limited number of big islands of one single color.

Techniques are aimed at rendering more natural the appearance ofhalftoned images. According to some techniques (E.g., PARAWACS), thefinal device state is modified by using weights obtained from a randommatrix: hence, the image appears less posterized, as there is the randompossibility that the final device state is modified for some pixels.According to other techniques (e.g., feedback-based techniques), thefinal device state is determined by sequentially selecting the pixelsaccording to a sequence and, after having chosen the final device statefor one selected pixel, updating the NPac vector for adjacent orneighbouring pixels which are to be selected subsequently. For example,after having chosen the final device state for the selected pixel, theprobability of non-chosen device states will be increased forsubsequently selected pixels, while the probability of previously chosenstates will be reduced for subsequently selected pixels. For example,if, for NPac₁, CM is chosen, the probability of choosing CM insubsequent (adjacent or neighbouring) pixels will be reduced, while theprobability of choosing other NPs will be increased.

In some of these feedback-based techniques, a so-called error diffusionmay be processed. Accordingly, the error in one pixel is summed to thesubsequent pixels. An example may be provided based on two adjacent orneighbouring pixels 1 and 2, associated to two device probabilityvectors NPac, (described above), and NPac₂. For pixel 1, withprobabilities described by NPac₁, CM, with probability 3/9, has beenchosen as final device state: the error for the component CM is 1− 3/9=6/9, and a value associated to 6/9 is therefore subtracted from theprobability associated to CM for pixel 2. For pixel 1, the error for thecomponent M is 0− 2/9=− 2/9, and a value associated to − 2/9 issubtracted from the probability of choosing M for pixel 2. Therefore,the error diffusion tends to decrease the probability of choosing CM andincrease the probability of choosing M for pixel 2.

The errors are often diffused to a multiplicity of subsequent, adjacentor neighbouring pixels. The errors are weighted using equal or differentweights. For example, the error 6/9 for the component CM may be scaled(e.g., through multiplication) by a fixed coefficient which may be ¼ (toobtain 6/9*¼=0.166667) and distributed to four subsequent, adjacent orneighbouring pixels. This is in an attempt of achieving a moredistributed error diffusion. In alternative, the method ofFloyd-Steinberg is also based on using fixed coefficients (weights):7/16 of the error is diffused to the pixel at the right of the selectedpixel; 3/16 of the error is diffused to the pixel at the bottom-left;5/16 of the error is diffused to the pixel below; and 1/16 is diffusedto the pixel at the bottom-right. Different weight distributions mayalso be conceived.

There is not one single weight distribution which is optimal for anyimage. In some cases, it is preferable to maintain the lines of theimage, while in other cases it is preferred to increase sharpness.

It has been noted that artefacts may be dependent on the content of theimage. For example, lighter tones are more prone to generate repetitivepatterns, which appear unpleasant to human eye.

Further, in some error diffusion techniques, there arises thepossibility of choosing an evidently incorrect NP: a large error in oneNP can propagate in a wrong direction, even to pixels which shall notpresent that NP, by virtue of error accumulation at previous pixels.Accordingly, the contour of the edges may result imprecise.

Techniques which permit to select appropriate weight distributions onthe basis of the content of the images are here presented.

FIG. 1 shows a device 100. The device 100 may be a device for halftoningand, in case, printing. The device 100 may convert an image 102 definedin a color space (e.g., a bitmap) or an image 104 defined in a devicestate probability space (e.g., NPacs) into an image 106 defined in adevice state space (e.g., NPs chosen for each pixel).

In some examples, the device 100 may comprise a device state probabilitygenerator 110, which may transform pixels defined in a color space(e.g., RGB, CMY, etc.) into pixels defined in a device state probabilityspace (e.g., a space defined in terms of NPacs). A lookup table, LUT,112 may be used by the device state probability generator 110, whichassociates colors (e.g., as defined in terms of R, G, B components) intostatistical values (e.g., as defined in terms of probabilities ofchoosing each NP). The LUT 112 may be stored in a storage device (e.g.,a non-transitory storage device) and/or may be based on calibration dataobtained during a calibration session. In some examples, the elements110 and 112 are not used, and the device 100 may directly receive theimage 104 defined in terms of device state probabilities.

The device 100 may comprise a pixel selector 114. The pixel selector 114may iteratively select, one after the other, pixels of the image 104(e.g., all the pixel of the image). The pixel selector 114 may choose afirst pixel (seed) which may be, for example, the pixel at the most leftand highest position among the all pixels of the image. Subsequently,other pixels of the image will be sequentially chosen, according to apath which may be defined a priori. For example, a serpentine path maybe used: after having selected one pixel, the subsequent pixel may be inthe bottom, in the right or in the left. Pixel by pixel, all the pixelsof the image (or at least a majority thereof) will be selected. The pathmay be boustrophedonic: when arriving at the margins of the image, thepixels are selected in reverse direction. Reference numeral 116indicates the particular pixel which has been selected at eachiteration.

The device 100 may comprise an error diffuser 118. The error diffuser118 may subtract previously obtained errors (in a weighted version) fromthe device state probabilities (NPacs). The error diffuser 118 mayprovide, for the selected pixel 116, a NPac 120 which keeps into accounterrors (residuals) associated to determinations carried out of thepreviously selected pixels. It is noted, however, that it is notstrictly necessary that the error is diffused to the pixels that will beselected immediately subsequently: the error is diffused to pixels whichwill be subsequently selected, even if not necessarily at theimmediately following iteration.

The device 100 may comprise a device state determiner 122. The devicestate determiner 122 may choose, for the selected pixel, one particulardevice state. The choice may be carried out by keeping into account theprobabilities (updated with the diffused errors) associated to eachdevice state. Basically, by choosing the final NP from each NPac, aconversion from NPacs into NPs is performed. The conversion may followadditional steps, such as random or pseudo-random modifications. Forexample, the final device state may be modified by modifying NPacs withfurther weights obtained from a random matrix (e.g., a matrix normallyused for PARAWACS techniques). In other cases, the device statedeterminer 122 may choose the device state associated to the NP with thehighest probability in the NPac, for example.

A storage device may be used to store, iteration by iteration, thedevice state of each selected pixel, so as to form an image 106 definedas a collection of NPs.

The chosen device state is one of the NPs for each selected pixel. If CMis chosen, then the printer will generate a halftone pixel with one dropof Cyan and one drop of Magenta combined with each other.

The device 100 may present a feedback chain formed by a plurality ofcomponents aimed at diffusing the device state probability error. Thefeedback chain may comprise, inter alia, the error diffuser 118, aselected pixel error determiner 124, a neighbouring pixel selector 130,and a neighbouring pixel error modifier 128. At each iteration, thefeedback chain prepares error information which is to be used atsubsequent iterations.

The device 100 may comprise a selected pixel error determiner 124, todetermine the device state probability error for each device stateprobability of the selected pixel. The device 100 may use information123, provided by the device state determiner 122, indicating which NPhas been chosen. For example, if a particular NP has been chosen (e.g.,CM), the selected pixel error determiner 124 will be aware of that.

The selected pixel error determiner may obtain the device stateprobability error (residual) 126 for each NP of the NPac associated tothe selected pixel. The selected pixel error determiner 124 may obtainboth the knowledge of the NP chosen at the device state determiner 122and the knowledge of the NPac 116 from which the NP has been extracted.Therefore, the selected pixel error determiner 124 may be input with theinformation 123 on the chosen NP and the NPac originally associated tothe selected pixel 116. In particular, the input 116 refers to the NPacupstream to the error diffuser 118, i.e., the NPac as defined by thedevice state probability generator 110 (and not the NPac after errordiffusion).

The error associated to the chosen NP (e.g., the CM in the vector NPac₁)may be calculated as 1−NP_(CM), where NP_(CM) is the probability ( 3/9)associated to CM in vector NPac₁. The error associated to the non-chosenNPs will be 0−NP_(i) (with NP_(i) being the probability associated toeach i^(th) NP, excluding CM, which has been selected).

The device 100 may comprise a neighbouring pixel selector 130. Theneighbouring pixel selector 130 may be input with information regardingthe selected pixel 116. The neighbouring pixel selector 130 maytherefore choose a collection of neighbouring pixels 132 which are inthe neighbourhood to the selected pixel 116. For example, pixelsadjacent or neighbouring to the selected pixel may be identified by theneighbouring pixel selector 130. For example, the pixel at the rightside of the selected pixel, the pixel below the selected pixel, etc.,may be chosen as neighbouring pixels. Notably, however, the neighbouringpixel selector 130 is not necessarily bounded to select, as neighbouringpixels, those pixels which are immediately adjacent to the selectedpixel 116. In examples, the neighbouring pixel selector 130 may select,as neighbouring pixels, an area in which some pixels are not immediatelyadjacent to the selected pixel 116. In examples, the neighbouring pixelselector 130 may, for example, vary the area of neighbouring pixels fordifferent iterations. In examples, the selected neighbouring pixels arepixels which are not already selected by the pixel selector 114, and forwhich the final device state (NP) has not been determined at the currentiteration. The neighbouring pixels 132 are pixels which will besubsequently subjected to error diffusion on the basis of the currentlyselected pixel 116. However, the order according to which theneighbouring pixel selector 130 selects the neighbouring pixels is notnecessarily the same of the order (e.g., boustrophedonic order)according to which the pixel selector 114 selects the pixels to beprocessed: therefore, it is not strictly guaranteed that theneighbouring pixels 132 will be processed in the immediately subsequentiterations. In examples, the selection of the neighbouring pixels ispre-established and/or fixed.

The device 100 may comprise a neighbouring pixel error modifier 128. Theneighbouring pixel error modifier 128 is in general aware of theneighbouring pixels 132 which will be subjected to error diffusion. Theneighbouring pixel error modifier 128 is in general aware of the devicestate probability errors 126 provided by the selected pixel errordeterminer.

The neighbouring pixel error modifier 128 may provide, during subsequentiterations, error information 134 to the error diffuser 118, so as todiffuse the probability errors 126 to the NPacs 116 associated to thepixels which will be selected in subsequent steps. Basically, the errorinformation 134 will be provided to the error diffuser 118 after that,in subsequent iterations, the pixel selector 114 has selected thosepixels which, at the current iteration, are the neighbouring pixel 132selected by the neighbouring pixel selector 130. Therefore, insubsequent iterations, the selected pixels 116 will be subjected toerror information 134 based on the device state probability errors 126.

In general terms, the error information 134 may be a weighted version ofthe device state probability error 126. The weights may be, for example,coefficients in the interval [0, 1]. According to a user's selection,the weights may be chosen so that their sum is 1, greater than 1, orlower than 1. The weights may be distributed among the neighbouringpixels 132 selected by the neighbouring pixel selector 130.

More in general, the neighbouring pixel error modifier 128 may modifythe probability errors 126 so as to diffuse the probability errors 126differently to each neighbouring pixel 132.

In examples, the neighbouring pixel error modifier 128 may diffuse theprobability errors 126 according to different criteria. For example,different criteria may cause a different distribution of weights, withpossible different consequences on the impression provoked to a humanobserver.

In examples, the criteria may be defined so as to be conditioned bymetrics 138 and/or by classification data 140. Different metrics ordifferent classification data may imply a different distribution ofweights.

Hence, a dynamic or content-based distribution of weights may beachieved: the weights are distributed according to a fashion which isdetermined by the content itself, and are, in general not constant fordifferent selected pixels and not necessarily equal to each other forthe same selected pixel.

FIGS. 2(a)-2(d) show different patterns of pixels that may be processedaccording to examples associated to different criteria for diffusing theprobability errors. While the patterns show four neighbouring pixels foreach selected pixel, different numbers of pixels may be chosen accordingto different examples. For example, the number of pixels may be varied,e.g., according to design options.

FIG. 2(a) shows a pattern 200 a of pixels according to an example. Acurrently selected pixel 202 (which may correspond to the selected pixel116 for which, at a current iteration, the final device state is to bechosen) permits, after having determined the final device state (NP) tobe used pro printing, to determine how to diffuse device stateprobability errors to neighbouring pixels 204-210. The final devicestate for neighbouring pixels 204-210 is not determined at the currentiteration. However, at the current iteration it is intended to determinehow to diffuse the device state probabilities error among theneighbouring pixels 204-210 (e.g., how to determine weights which willscale the device state probability error 126).

According to examples, criteria may modify the error distribution amongthe neighbouring pixels 204-210 in function of metrics based on theoriginal color data (e.g., expressed in R, G, B coordinates) of theselected pixel in the image 102. For example, the weight of theneighbouring pixel 204 may be expressed as f1(RGB); the weight of theneighbouring pixel 206 may be expressed as f2(RGB), and so on.

For example, it is possible to choose different functions (weights) f1 .. . f4 on the basis of the tone of the pixel in the original image 102.In some examples, a less uniform error distribution may be chosen incase the selected pixel 202 has a lighter tone, and a more uniform errordistribution in case the selected pixel 202 has a darker tone.

E.g., it is possible to increase the weight f1 to the neighbouring pixel204 (pixel ahead) for lighter tones and reducing the weights f2 . . . f4of the neighbouring pixels 206-210 (pixels below and behind) in such away that the neighbouring pixels 206-210 appear out of synchronism withrespect to the selected pixel 202 and the neighbouring pixel 204. Inseveral cases of images with lighter tones, in fact, the result is aless regular, more pleasing pattern. A benefit is reducing clusteringand repetitive patterns.

To the contrary, if the tone of the selected pixel 202 has a reducedlightness, the criterion chooses more compact weights f1 . . . f4.

FIG. 3 shows an example of a neighbouring pixel error modifier 128 awhich generates the pattern of FIG. 2(a). The device state probabilityerrors 126 are modified, to obtain error information 134 to be providedto the error diffuser 118 according to criteria conditioned by colordata (e.g., tone) of the selected pixel 202 (116).

The neighbouring pixel error modifier 128 a may be input with metrics138 a which may comprise a tone of the selected pixel. The metrics 138 amay be provided by a metrics measurer 148 a, which may be, for example,a tone measurer. The tone measurer may determine the tone as the sum ofthe R, G, and B components of the pixel in the color space, e.g., as inthe original image 102.

The neighbouring pixel error modifier 128 a may comprise a weightdefiner 150 a which determines the functions f1 . . . f4 (weights) whichare to be associated to the neighbouring pixels 204-210 to properlymodify the probability error 123. In some examples, the lighter the toneof the selected pixel 202, the more differentiated are the weights amongdifferent neighbouring pixels.

The neighbouring pixel error modifier 128 a may comprise a device stateprobability error scaler 152 a, which scales (e.g., by multiplication)the device state probability errors 126 by the weights f1 . . . f4. Eachdevice state probability error 126 may be a vector of errors, eachassociated to a NP contained in the NPacs vector associated to theselected pixel 202. Each component of the device state probability error126 may be multiplied by each weight f1 . . . f4. Therefore, thereference numeral 134 refers in this example to four error vectors (eachof the four error vectors, associated to one of the neighbouring pixels204-210, being derived by multiplication of a respective device stateprobability error 126 with one of the four weights f1 . . . f4). Each ofthe four vectors will therefore be used in one of subsequent iterations,for diffusing the probability errors 126 to the neighbouring pixels204-210 (the four subsequent iterations will be those iterations forwhich one of the neighbouring pixels 204-210 will become the selectedpixel 116).

FIG. 2(b) shows a pattern 200 b of pixels according to an example, witha currently selected pixel 202 and neighbouring pixels 204-210.

According to examples, the criteria may modify the error distributionamong the neighbouring pixels 204-210 in function of metrics based onthe device state probabilities (e.g., NPacs) of the selected pixel inthe image 104. For example, the weight of the neighbouring pixel 204 maybe expressed as n1(NPac); the weight of the neighbouring pixel 206 maybe expressed as n2(NPac), and so on.

For example, it is possible to choose different functions n1 . . . n4 onthe basis of the values of the components of the NPac of the pixel inthe image 104.

For example, if the greatest error among the components of the vectorforming the probability errors 126 is in MC, then an increased weightwill be assigned, among the neighbouring pixels 204-210, to that pixelfor which the device state probability of MC is the highest. A reducedweight will be given to the pixels having a low device state probabilityof MC.

In examples, increased weight may be assigned to a neighbouring pixelwhich has tendentially higher device state probabilities correspondingto device states associated to higher probability errors.

In examples, NPs with higher errors may be associated to neighbouringpixels having higher probability for the same NPs.

In examples, the criteria may define that, if a NP probability error isover a NP probability error threshold, higher weights will be assignedto neighbouring pixels having the probability for the same NP over a NPprobability threshold.

More in general, the error diffusion may be based on correspondencesbetween each of the device state probability errors associated to theselected pixel and each of the device state probabilities for each ofthe neighbouring pixels. Among the neighbouring pixels 204-210, agreater weight may be assigned to the pixel with most of thecorrespondences between the errors in the NPs (at the selected pixel)and the probabilities of the NPs in the NPac vector (at the neighbouringpixel).

In particular, the probability of choosing an evidently incorrect devicestate is reduced: a large error in one NP will not necessarily cause theprint of the NP in a pixel where it is not intended. The occurrence ofthe choice of a device state that has zero probability at a pixel may beavoided. Therefore, an increased sharpness is obtained. In particular,the edges and the textures are better preserved.

FIG. 4 shows an example of a neighbouring pixel error modifier 128 bwhich generates the pattern 200 b of FIG. 2(b). The neighbouring pixelerror modifier 128 b may modify device state probability errors 126 toobtain error information 134 to be provided to the error diffuser 118according to criteria conditioned by the NPacs of the neighbouringpixels.

The neighbouring pixel error modifier 128 b may be input with metrics138 b which may comprise metrics associated to the probability of eachNP for the neighbouring pixels 204-210. The neighbouring pixel errormodifier 128 b may be input with metrics 138 b which may comprisemetrics associated to the NP probability errors as obtained by theselected pixel error determiner 124. The metrics 138 b may be providedby a metrics measurer 148 b which comprises a first metrics measurer 148b′ which, for each NP, takes into account the probably of each NP, and asecond metrics measurer 148 b″, which takes into consideration the errorfor each NP. The metrics measurer 148 b may provide, for eachneighbouring pixel, the NP with highest probability (138 b′). Themetrics measurer may provide the probability error 138 b″ associated toeach NP of the selected pixel 202 (116).

The neighbouring pixel error modifier 128 b may comprise a comparer andweight definer 150 b which may choose functions n1 . . . n4 to beassociated to the neighbouring pixels 204-210 on the basis ofcorrespondences between NP probabilities of the neighbouring pixels andthe NP probability errors of the selected pixel 202 (116). In examples,the highest weight may be assigned to the neighbouring pixel having thehighest probability for the NP which is subjected to the maximum NPprobability error.

The weights n1 . . . n4 may be used at scaler 152 b to scale NPprobability errors 126, which may be subsequently used for diffusing theerrors differently for the different neighbouring pixels.

FIG. 2(c) shows a pattern 200 c in which the error diffusion isdetermined, for each of the neighbouring pixels 204-210, on the basis ofthe device state value chosen for the selected pixel 202 (116) (e.g., bythe device state determiner 122). In this case, the error diffusion mayinterest a reduced group among the components of the NPac vector. Forexample, weights g1 . . . g4 are used for some particular NPs, and arenot used (or are defined as 0) for different NPs. Let be assumed, forexample, that only NPs with C are chosen. Therefore, the error will bediffused only in the components C, CM, CY, but the no error diffusionwill be performed on the components Y, M, YM, B, as no C is implied.This is how to limit the error diffusion to a particular vectorialsubspace, while avoiding the error diffusion in the complementaryvectorial subspace. Hence, if in the NPac some NP have zero probability,they will not participate to error diffusion.

Hence, it is possible to define different weights for differentprobabilities associated to the same selected pixel.

In examples, the criteria for diffusing the errors may be different fordifferent device states. Just to give an example, it may be possible touse first criteria (e.g., associated to the tone of the selected pixel)for a first group of NPs, and second criteria for a second group of NPs.

Accordingly, it is possible to optimize the print for a particular ink,hence resulting in smoother prints.

FIG. 4 shows a pattern 200 d in which, according to the criteria, errordiffusion is determined, for each of the neighbouring pixels 204-210, onthe basis of metrics associated to some particular attributes of thepixel or a neighbourhood of pixels. The weights, here identified as l1 .. . l4, may vary according to the attribute.

For example, it is possible to check whether the error diffusion willchange attributes of the neighbouring pixel. For example, it is possibleto try a plurality of error diffusion mode candidates and to choose theerror diffusion mode which least modifies the attribute. For example, itis possible to assign increased weights to the neighbouring pixels whichleast change the attribute. For example, in case the criteria is tominimize the lightness contrast between neighbouring pixels, largerweights are provided to those neighbouring pixels which would minimizethe change in lightness contrast.

FIG. 5 shows a neighbouring pixel error modifier 128 d which may be usedfor obtaining the pattern 200 d according to a criteria based on aparticular attribute. A weight definer 150 d may define a collection ofweights l1 . . . lk. These weights may scale, at scaler 152 d, thedevice state probability errors 126, to obtain error information 134.The weight definer 150 d, through the command 160, maintains open aswitch 162 which does not permit to provide the error information 134 tothe error diffuser 118. However, the obtained error information 134 maybe provided to an attribute simulator 164, which simulates the resultsof the print (with the particular weights l1 . . . lk) for a particularattribute (e.g., lightness contrast). Metrics 138 d associated to theattribute may be measured by the metrics measurer 148 d. On the basis ofthe metrics 138 d, the weight definer 150 d may decide for a differentcollection of weights l1 . . . lk. Hence, a plurality of iterations areattempted for obtaining the best collection of weights. When the bestcollection of weights is determined (e.g., that that minimizes thelightness contrast), the switch 162 may be closed by command 160 and theweighted error information 134 may be provided to the error diffuser118. By examining the result for several simulations, the mostappropriated error diffusion mode may be selected.

In additional or alternative, the weight definer 150 d may choose decideamong a plurality of criteria, such as one of those discussed above. Thechosen criterion may be the one that permits to obtain the minimizationof the lightness contrast.

Back to FIG. 1, classification data 140 may be used in addition oralternative to metrics 138.

For example, each pixel may be associated to classification data such as“line”, “photographic content”, “text”, “business”, etc. Theclassification data may be used to define different error diffusionmodes (e.g., different weights) for different neighbouring pixels. Forexample, if the selected pixel 202 and the neighbouring pixel 204 areclassified as “line” while the neighbouring pixels 206-210 are notclassified as “line”, a greater amount of the error will be diffusedfrom the selected pixel 202 to the neighbouring pixel 204, while areduced amount of error will be diffused to the remaining neighbouringpixels 206-210.

In the examples above, reference has been made to metrics associated tothe currently selected pixel. In addition or alternative, the metricsmay also relate to previously selected pixels or to both previouslyselected pixels and the currently selected pixel. For example, aggregatedata (e.g., integrals data) and/or statistical data (e.g., averagevalues) may be used as metrics for calculating the weights.

FIG. 6 shows a method 600. The method 600 may be implemented, forexample, using equipment discussed above. At step 602, the method maycomprise iteratively selecting pixels of an image. Each pixel may beassociated to a vector of device state probabilities. Each element ofthe vector may relate to a probability of choosing a particular devicestate. Each device state may describe the quantity of each colorant tobe used in correspondence of the pixel. At step 604, the device state ischosen for a currently selected pixel on the basis of the device stateprobabilities associated to the selected pixel. At step 606, devicestate probability errors are diffused to pixels to be selected insubsequent iterations. For example, it is possible to weight differentlyerrors diffused in different pixels according to measurements obtainedfrom the currently selected pixel and/or on previously selected pixelsand/or classification data.

FIG. 7 shows a processor-based system 700. The system 700 may comprise aprinter 702 and a processor-based system 704 comprising a processor 706.The processor 706 may communicate with the printer through aninput/output, I/O, port 708. The processor-based system 704 may comprisea non-transitory storage unit 710. The non-transitory storage unit 710my comprise instruction which, when executed by the processor 706, mayperform the method 600. In particular, the instructions 600 may guidethe processor to select a pixel associated to device state probabilitiesdescribing probabilities of choosing particular device states describingquantities of colorant to be used in correspondence of the pixel.Further, the instructions may guide the processor to choose a devicestate for the pixel on the basis of the device state probabilities. Theinstructions may cause the processor to diffuse device state probabilityerrors to pixels to be subsequently selected. The instructions may causethe processor to determine, for the pixels to be subsequently selected,device state probability errors. The instructions may cause theprocessor to choose neighbouring pixels among the pixels to besubsequently selected. The instructions may cause the processor tomodify a neighbouring pixel error, to modify, for each neighbouringpixel, the determined device state probability errors according toweights conditioned by metrics and/or classification data associated tothe pixel and/or a group of previously selected pixels and the relativeposition between the neighbouring pixel and the selected pixel. Theinstructions may cause the processor to diffuse the modified devicestate probability errors to the neighbouring pixels.

Generally, examples can be implemented as a computer program productwith a program code, the program code being operative for performing oneof the methods when the computer program product runs on a computer. Theprogram code may for example be stored on a machine readable carrier.

Other examples comprise the computer program for performing one of themethods described herein, stored on a machine readable carrier.

In other words, an example of method is, therefore, a computer programhaving a program code for performing one of the methods describedherein, when the computer program runs on a computer.

A further example of the methods is, therefore, a data carrier (or adigital storage medium, or a computer-readable medium) comprising,recorded thereon, the computer program for performing one of the methodsdescribed herein. The data carrier, the digital storage medium or therecorded medium are typically tangible and/or non-transitionary.

A further example of the method is, therefore, a data stream or asequence of signals representing the computer program for performing oneof the methods described herein. The data stream or the sequence ofsignals may for example be configured to be transferred via a datacommunication connection, for example via the Internet.

A further example comprises a processing means, for example a computer,or a programmable logic device, configured to or adapted to perform oneof the methods described herein.

A further example comprises a computer having installed thereon thecomputer program for performing one of the methods described herein.

A further example comprises an apparatus or a system configured totransfer (for example, electronically or optically) a computer programfor performing one of the methods described herein to a receiver. Thereceiver may, for example, be a computer, a mobile device, a memorydevice or the like. The apparatus or system may, for example, comprise afile server for transferring the computer program to the receiver.

In some examples, a programmable logic device (for example a fieldprogrammable gate array) may be used to perform some or all of thefunctionalities of the methods described herein. In some examples, afield programmable gate array may cooperate with a microprocessor inorder to perform one of the methods described herein. Generally, themethods are preferably performed by any hardware apparatus.

The apparatus described herein may be implemented using a hardwareapparatus, or using a computer, or using a combination of a hardwareapparatus and a computer.

The apparatus described herein, or any components of the apparatusdescribed herein, may be implemented at least partially in hardware.

The methods described herein may be performed using a hardwareapparatus, or using a computer, or using a combination of a hardwareapparatus and a computer.

The methods described herein, or any components of the apparatusdescribed herein, may be performed at least partially by hardware.

The above described examples are merely illustrative for the principlesdiscussed above. It is understood that modifications and variations ofthe arrangements and the details described herein will be apparent toothers skilled in the art. It is the intent, therefore, to be limitedonly by the scope of the impending patent claims and not by the specificdetails presented by way of description and explanation of the examplesherein.

The invention claimed is:
 1. A device comprising: a pixel selector, toselect a pixel from an image to be printed, wherein the pixel isassociated to a group of device state probabilities, wherein each devicestate probability describes a probability of choosing a particulardevice state, wherein each device state describes the quantity of eachcolorant to be used in correspondence of the pixel; a device statedeterminer, to choose the device state for the selected pixel on thebasis of the device state probabilities associated to the selectedpixel; a feedback chain, to diffuse device state probability errors topixels to be subsequently selected, wherein the feedback chain includes:a selected pixel error determiner, to determine device state probabilityerrors for each device state probability of the selected pixel; aneighbouring pixel selector, to choose neighbouring pixels among thepixels to be subsequently selected; a neighbouring pixel error modifier,to modify, for each neighbouring pixel, the determined device stateprobability errors according to criteria conditioned by: metrics and/orclassification data associated to the selected pixel and/or a group ofpreviously selected pixels; and the relative position between theneighbouring pixel and the selected pixel; and an error diffuser, todiffuse the modified device state probability errors to the neighbouringpixels.
 2. The device of claim 1, wherein the neighbouring pixel errormodifier is to modify the determined device state probability errorsaccording to criteria conditioned by color data of the image to beprinted.
 3. The device of claim 1, wherein the neighbouring pixel errormodifier is to modify the determined device state probability errorsaccording to criteria conditioned by the tone of the selected pixel, thecriteria being so as to cause, among the neighbouring pixels: a lessuniform error distribution among the neighbouring pixels in case theselected pixel has a lighter tone; and a more uniform error distributionamong the neighbouring pixels in case the selected pixel has a lesslight tone.
 4. The device of claim 1, wherein the neighbouring pixelerror modifier is to modify the determined device state probabilityerrors according to criteria conditioned by device state probabilitiesof the image to be printed.
 5. The device of claim 1, wherein theneighbouring pixel error modifier is to modify the determined devicestate probability errors according to criteria to diffuse the devicestate probability errors on the basis of correspondences between: eachof the device state probability errors associated to the selected pixel;and each of the device state probabilities for each of the neighbouringpixels.
 6. The device of claim 1, wherein the neighbouring pixel errormodifier is to modify the determined device state probability errorsaccording to criteria to diffuse the device state probability errors sothat, if a device state probability error is over a device stateprobability error threshold, higher weights are given to neighbouringpixels having the probability, for the same device state, over a NPprobability threshold.
 7. The device of claim 1, wherein theneighbouring pixel error modifier is to modify the determined devicestate probability errors so as to: increase weights for neighbouringpixels whose higher device state probabilities correspond to devicestates associated to higher probability errors; and reduce weights forneighbouring pixels whose higher device state probabilities correspondto device states associated to lower probability errors.
 8. The deviceof claim 1, wherein the neighbouring pixel error modifier is to modifythe determined device state probability errors according to criteriawhich are different for different probabilities associated to the sameselected pixel.
 9. The device of claim 1, wherein the neighbouring pixelerror modifier is to avoid error diffusion towards pixels which havezero probability for a selected colorant.
 10. The device of claim 1,wherein the neighbouring pixel error modifier is to modify thedetermined device state probability errors on the basis of criteriabased on multiple simulations on the effects of different modes forerror diffusion, wherein the neighbouring pixel error modifier is tomodify the determined device state probability errors based on a modefor error diffusion that best conforms to the criteria.
 11. The deviceof claim 1, wherein the neighbouring pixel error modifier is to modifythe determined device state probability errors on the basis of multiplesimulations performed with different criteria, wherein the neighbouringpixel error modifier is to modify the determined device stateprobability errors based on a mode for error diffusion that bestconforms to a respective criterion of a respective simulation.
 12. Thedevice of claim 1, wherein the neighbouring pixel error modifier is tomodify the determined device state probability errors on the basis ofmetrics based on simulation of the lightness contrast among pixels,wherein the neighbouring pixel error modifier is to modify thedetermined device state probability errors to correspond to a minimizedlightness contrast among pixels.
 13. The device of claim 1, wherein theneighbouring pixel selector is to select a number of neighbouring pixelsbased on a selected error diffusion distance.
 14. A method comprising:iteratively selecting pixels of an image, each pixel being associated toa vector of device state probabilities, wherein each element of thevector relates to a probability of choosing a particular device state,wherein each device state describes the quantity of each colorant to beused in correspondence of the pixel; at each iteration, choosing thedevice state for a currently selected pixel on the basis of the devicestate probabilities associated to the selected pixel; diffusing devicestate probability errors to pixels to be selected in subsequentiterations by weighting differently errors diffused in different pixelsaccording to measurements obtained from the currently selected pixeland/or on previously selected pixels and/or classification data.
 15. Anon-transitory storage unit storing instructions which, when executed bya processor, cause the processor to: select a pixel associated to devicestate probabilities describing probabilities of choosing particulardevice states describing quantities of colorant to be used incorrespondence of the pixel; choose a device state for the pixel on thebasis of the device state probabilities; diffuse device stateprobability errors to pixels to be subsequently selected; determine, forthe pixels to be subsequently selected, device state probability errors;choose neighbouring pixels among the pixels to be subsequently selected;modify a neighbouring pixel error, to modify, for each neighbouringpixel, the determined device state probability errors according toweights conditioned by: metrics and/or classification data associated tothe pixel and/or a group of previously selected pixels; and the relativeposition between the neighbouring pixel and the selected pixel; anddiffuse the modified device state probability errors to the neighbouringpixels.